Associate Professor Zigan Wang

Associate Professor Zigan Wang is an economist and interdisciplinary researcher whose work bridges economics, business, and computer science. His research focuses on applied microeconomics, international and environmental economics, political economy, econometrics, and emerging technologies such as computer vision and generative adversarial networks (GANs).

Dr. Wang earned his Ph.D., M.Phil., and M.A. from Columbia University, following a B.A. from Tsinghua University. He has published in leading economics and business journals including Management Science, Journal of Financial Economics, Journal of Financial and Quantitative Analysis, Review of Finance, Journal of Econometrics, Journal of International Economics, etc. and leading computer science conferences including CVPR and KDD. His recent work explores international economics, corporate finance, econometrics, statistics, computer vision and knowledge graph. His research has been recognized with multiple awards, including the Bureau van Dijk Prize in Corporate Finance at the UNSW Australian Finance and Banking Conference (2018, 2021) and the Faculty Research Postgraduate Supervision Award at The University of Hong Kong (2021, 2022). Dr. Wang has also secured competitive research grants, such as the Hong Kong General Research Fund (three times) and China National Science Fund for Distinguished Young Scholars.

As an active member of professional societies like the American Economic Association, American Finance Association, European Finance Association, Econometric Society, ACM, and IEEE, Dr. Wang welcomes students interested in interdisciplinary research at the intersection of economics, business and computer science.

Besides his pipeline research on foreign exchange and corporate business management, Associate Professor Zigan Wang's current research interests are mainly as follows:

1. Graphical neural network analysis of systematic financial risks in economic networks.
2. Analysis of financial data security and privacy computing across financial institutions.
3. Computational analysis of business- and genomics-related data.